Establishment of an Effective Brain Tumor Classification System through Image Transformations and Optimization Techniques

Arora, Gaurav Kumar; Mahaboob, Shaik Taj; Adilakshmi, S.; Rani, Shaveta; Kasthuri, A.; Mamodiya, Udit

doi:10.1109/ihcsp56702.2023.10127182

Cited by 2 publications

(2 citation statements)

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“…The Fusion-Head Self-Attention Mechanism (FHSA) leverages the long-range spatial dependency of 3D MRI data. The self-attention module known as the Infinite Deformable Fusion Transformer Module (IDFTM) is designed to extract features from deformable feature maps in a plug-and-play manner [33], [34]. The proposed technique demonstrated superior performance compared to leading segmentation algorithms on publicly accessible BRATS datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The tumor portions exhibit distinct features such as reduced dimensions, asymmetrical contours, and comparable textures to the adjacent normal tissues. Due to these circumstances, the existing techniques employed for the segmentation of the ET and TC regions do not consistently yield the desired outcomes [32], [33], [34]. In light of the aforementioned constraints, the primary objective of this study is to introduce an innovative deep learning-driven framework that enhances the accuracy of segmenting both the increased and core tumor regions in brain tumors [35], [36], [37].…”

Section: Introductionmentioning

confidence: 99%

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Dual Vision Transformer-DSUNET With Feature Fusion for Brain Tumor Segmentation

Zakariah,

Al-Razgan,

Alfakih

2024

Preprint

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Brain tumors are one of the leading causes of cancer death; screening early is the best strategy to diagnose and treat brain tumors. Magnetic Resonance Imaging (MRI) is extensively utilized for brain tumor diagnosis; nevertheless, achieving improved accuracy and performance, which is a critical challenge in most of the previously reported automated medical diagnostics, is a difficult problem. The study introduces the Dual Vision Transformer-DSUNET model, which incorporates feature fusion techniques to provide precise and efficient differentiation between brain tumors and other brain regions by leveraging multi-modal MRI data. The impetus for this study arises from the necessity of automating the segmentation process of brain tumors in medical imaging, a critical component in the realms of diagnosis and therapy strategy. To tackle this issue the BRATS 2020 dataset is employed, an extensively utilized dataset for the segmentation of brain tumors. This dataset encompasses multi-modal MRI images, including T1-weighted, T2-weighted, T1Gd (contrast-enhanced), and FLAIR modalities. The proposed model incorporates the dual vision idea to comprehensively capture the heterogeneous properties of brain tumors across several imaging modalities. Moreover, the utilization of feature fusion techniques is implemented to augment the amalgamation of data originating from several modalities, hence enhancing the accuracy and dependability of tumor segmentation. The evaluation of the Dual Vision Transformer-DSUNET model's performance is conducted by employing the Dice Coefficient as a prevalent metric for quantifying segmentation accuracy. The results obtained from the experiment exhibit remarkable performance, with Dice Coefficient values of 91.47% for enhanced tumors, 92.38% for core tumors, and 90.88% for edema. The cumulative Dice score for the entirety of the classes is 91.29%. In addition, the model has a high level of accuracy, roughly 99.93%, which underscores its durability and efficacy in the task of segmenting brain tumors. Experimental findings demonstrate the integrity of the suggested architecture, which has quickly improved the detection accuracy of many brain diseases.

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Section: Literature Reviewmentioning

confidence: 99%